1. Technical Field
The present disclosure relates to color measurement and color correction/color transformation in an imaging process, such as photography.
2. Background Art
Accurately estimating the color appearance in a scene that has been photographed has always been a challenge. Two conventional approaches exist to estimate color.
The first conventional approach to estimate color is to understand the process of capturing the color well enough so that the colors in the scene can be estimated based on the captured colors and knowledge of illumination source. For a digital camera, this method may require knowledge of the camera's spectral sensitivity as well as any non-linearity that existed. For example, photographers can use standardized transformations to correct/transform colors for different types of lighting, such as incandescent light, florescent light, sunlight, etc., e.g., by applying white balance. Unfortunately, the accuracy of such transformations is limited and often does not account for illumination angles and the “Bidirectional Reflectance Distribution Function” (BRDF) of the measured surface.
The second conventional approach to estimate color is to place known colors in a photographed scene and then create a transform from the captured colors to the known colors. For example, a target with a set of known colors can be photographed with a scene and used to calculate the transform based on a deviation between the actual and photographed appearance of the colors in the target. This transformation can then be used to calibrate colors in future photographs of the same scene (i.e., under the same lighting characteristics).
For this second approach, a number of factors can contribute to the accuracy of the transform. One important factor is the illumination of the target. The illumination of the target may be non-uniform (for example, the illumination may be from one or more sources that are in different directions relative to the target). Notably, the perceived appearance of the colors on the target and the colors in the scene is a function of both the lighting angles relative to the surface and the viewing angles relative to the surface. A BRDF can be used to describe how light is reflected from opaque surfaces. In particular, the BRDF of a surface can model how an appearance of a surface (e.g., perceived color of a surface) changes based on illumination and viewing conditions. Thus, if the colors in the target are characterized by a different BRDF than the colors in the scene or if the target is at a different viewing angle, the accuracy of a conventionally computed transformation may be insufficient.
U.S. Publication No. 2013/0093883 to Wang, et al., (“Wang”) discloses a “BRDF reference chart” which includes a set of materials with different reflection properties (color, material, texture, finish, etc.). Reference reflectance response distribution functions are matched to calculated reflectance responses, and an image of the target is reconstructed based at least in part on the matched reference reflectance response distribution functions. The reference chart in Wang, however, fails to enable or facilitate determination of illumination conditions for an imaged scene (evidenced by the fact that the reference chart in Wang is imaged at a plurality of viewing angles).
Thus, there exist needs for new and improved apparatus, systems and methods which increase the accuracy of color estimation and transformation in an imaging process, such as photographic processes. These and other needs are addressed by the present disclosure.